Wavelet transform and pattern recognition method for heart sound analysis

ABSTRACT

A wavelet transform and pattern recognition method for analyzing a subject&#39;s heart sounds including (a) obtaining subject-related heart-sound data utilizing a first sampling rate, (b) obtaining simultaneously existing subject ECG data, including pre-selected ECG fiducial data, and (c) processing such obtained data including, relative to the heart-sound data, (1) computing the maximum-overlap discrete wavelet transform (MODWT) for a preselected number of wavelet scales, (2) locating the peaks in time of the absolute values of the MODWT coefficients respecting each of a such scales, and (3), for each such scale, (i) interpolating between the located peaks, and (ii) subsampling each interpolation result at a second sampling rate which no greater than the mentioned first sampling rate.

CROSS REFERENCE TO RELATED APPLICATION

This application claims priority to prior-filed, currently U.S.Provisional Patent Application Ser. No. 60/772,046, filed Feb. 10, 2006,for “Wavelet Transform and Pattern Recognition Method for Heart SoundAnalysis”. The entire disclosure content of that Provisional Applicationis hereby incorporated herein by reference.

DEFINITIONS

CHMM settings. In the practice of the present invention, there are foursuch settings. These include: (a) penalty to prevent two S1 sounds fromoccurring within the same heartbeat (as defined by the interval betweensuccessive QRS onsets); (b) penalty to prevent S3 sounds from startingtoo soon or too late after the start of an S2 sound; (c) modificationsto transition probabilities that prevent the S1, S2, S3 and S4heart-sound times from starting at inappropriate times relative to QRSonset times; and (d) interbeat dependence which is a function thatboosts the probability score of a sound at a beat-relative time if thesame sound scored highly on previous beats at the same beat-relativetime. The natures of these settings are familiar to those skilled in theart.

Extended measurements. In the practice of the present invention, thereare four employed extended measurements. These include: (a) thewell-known quantity EMAT, which is the time duration from a QRS onset tothe associated, so-called S1 valve time; (b) % EMAT which is the ratioof EMAT divided by the duration from an R-peak time to the nextsuccessive R-peak time; (c) LVST which is the time duration measuredbetween the so-called S1 and S2 valve times; and (d) % LVST—a term whichrelates to the ratio of LVST divided by the duration from an R-peak timeto the next successive R-peak time.

Feature vector. This term, as employed herein, is something which iscalculated via a series of processing operations which will be morefully explained in the detailed description of the invention below.

Heart sounds. Also referred to herein as sound components, heart soundsinclude the usual, recognized S1, S2, S3, S4 heart-produced sounds.

Wavelet scale. A wavelet scale, as that term is employed herein, iseffectively a band of frequencies computed by a Length-8 wavelet filterdrawn from the Daubechies Least Asymmetric family of wavelet filters,with this band being bounded on its opposite ends by the minus 3-dbpoints in the associated frequency band relative to the operation of therelevant filter. Six wavelet scales, I-VI, inclusive, are involved ScaleI extends from about 124-Hz to about 250-Hz. Scale II extends from about62-Hz to about 124-Hz. Scale III covers a frequency band extending fromabout 31-Hz to about 62-Hz. Scale IV extends from about 16-Hz to about31-Hz. Scale V extends from approximately 8-Hz to approximately 16-Hz.Finally, scale VI includes a band of frequencies extending from about4-Hz to about 8-Hz.

Temporal window frame. With respect to each wavelet scale, a temporalwindow frame has a length herein of about 50-milliseconds. A series oftemporal window frames, generally speaking, is a series associated witha particular wavelet scale.

Overlapping. With respect to each wavelet scale, there is an associatedplurality of temporal window frames each having the length justmentioned above, and each overlapping one another in time whereby thebeginning of each frame occurs about 12-milliseconds after the beginningof the previous frame (assuming, of course, that there is a previousframe).

In the structure and the operation of the present invention, a certainkind of subsampling operation takes place with regard to theserespective wavelet scales, with the sampling rate for scale I beingabout 500-Hz, that for scale II being about 250-Hz, that for scale IIIbeing about 166.67-Hz, that for scale IV being about 83.33-Hz, that forscale V being about 41.67-Hz, and that for scale VI being about21.74-Hz.

BACKGROUND AND SUMMARY OF THE INVENTION

In the field of cardiology, there is strong and significant interest inutilizing the ever-improving capabilities of computer-based digitalsignal processing to aid in assessing the condition of the human heartand the associated cardiovascular system. The present invention is aimedat that interest.

In this context, the invention offers a unique, and informationallypowerful, methodology featuring a wavelet-transform and signal-patternrecognition approach which draws relevant cardio-condition informationfrom heart-sound data that is correlated in various ways withsynchronized, ECG, electrical-signal fiducials. Preferably, though notnecessarily, the source heart-sound and ECG data are derivednon-invasively from a human subject.

As one will learn on reading the below descriptive disclosure of thisinvention, the invention is characterized by a number of innovativefacets. With respect to these facets, the invention is easily visualizedand made understandable, and thus made readily practice-accessible tothe so-motivated users, by the block-schematic illustrations provided inthe two drawing-figure illustrations of the invention. Individualbuilding blocks used in these figures to present the importantarchitecture of the invention methodology may, per se, be entirelyconventional in internal construction and operation, but theircooperative, interrelated overall assembly and interactive operation(s)is/are unique. The above-mentioned invention facets are defined bydifferent sub-portions of this overall assembly.

Accordingly, the various advanced features and advantages of theinvention will now become more fully apparent as the description of theinvention which follows is read in conjunction with the associateddrawings.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified and very high-level block/schematic diagram ofthe overall architecture of the preferred embodiment of, and manner ofpracticing, the present invention.

FIG. 2 provides a more detailed block/schematic presentation of theinvention architecture which is illustrated in FIG. 1. Each individualblock in FIG. 2 is, per se, conventional and familiar to those who aregenerally skilled in the relevant art. For this reason, the respective,internal make-ups of these blocks are not further detailed herein.

DETAILED DESCRIPTION OF THE INVENTION

Turning now to the drawings, and beginning with FIG. 1, indicatedgenerally at 10 is a single block which represents the overallarchitecture of a preferred embodiment of, and manner of practicing, thepresent invention. Block 10, as a whole, takes the form of anappropriate, programmed, software-algorithm-controlled digital computer,within which, as illustrated by a dashed-lined rectangle 12, resides theoperative, architectural algorithm of the invention. This algorithmcarries out the central, wavelet-transform and signalpattern-recognition functionalities of the invention. Three inputs toblock 10 and to algorithm 12 are shown generally at 14, 16, 18, andfour, relevant, processed information outputs from block 10 andalgorithm 12, are shown generally at 20, 22, 24, 26.

Input 14 furnishes, preferably, non-invasive, heart-sound signals anddata, in any suitable, conventional manner, from a human subject. Input16 furnishes, preferably, non-invasively collected electrical ECGfiducial information which information is also delivered,simultaneously, in any suitable conventional manner, from the same humansubject. Input 18 furnishes conventional CHMM (Constrained Hidden MarkovModel) settings associated with the same human subject. These CHMMsettings have been defined earlier and above herein.

Adding attention now to FIG. 2 in the drawings, this figure elaboratesboth the core structure and the operating methodology of the invention,as such are embodied and realized in algorithm 12. As can be seen inthis figure, heart-sound input 14, and ECG fiducial-data input 16, eachfeeds input information to two different recipient locations inalgorithm 12. Algorithm 12, per se, includes sixteen word-labeled,operatively interconnected structural and functional blocks 28-58 (evennumbers only), inclusive. Each of these blocks herein in FIG. 2 isinternally conventional in construction, and the operations andstructures of these blocks, which may take on a number of differentconventional forms, are well understood by those generally skilled inthe relevant art.

The locations of previously described inputs and outputs are clearlymarked in FIG. 2, and with respect to inputs 14 and 16, and as was justearlier suggested herein, each of these two inputs connects,effectively, to two locations within the block diagram of FIG. 2 whichrepresents algorithm 12. Very specifically, heart-sound informationprovided by input 14 is supplied to blocks 28, 54 in FIG. 2, and ECGfiducial information, or data, is supplied by input 16 to blocks 40, 58.Made clearly evident in FIG. 2 are the operative, information-flowinterconnections, or paths, which exist between the several blocks, withthese signal-flow paths being illustrated by single-headed arrows whichdescribe the directionality of signal flow, and therefore of signalprocessing flow.

With respect to interconnections that exist between blocks 28 and 30, 30and 32, 34 and 36, and 36 and each of blocks 38, 40. Theseinterconnections are represented by collections of plural signal-flowarrows, with all of these arrow-“collections”-connections being six innumber between (a) block 28, 30, (b) blocks 30 and 32, (c) blocks 32,34, and (d) blocks 34, 36. This number of connections—six is related tothe above-mentioned six wavelet scales which are employed in thepractice of this invention. Between blocks 36, 38, the collection ofsignal-flow arrows is only three in number, and between blocks 36, 40,five in number. The reasons for the less-than-six numbers of connectionsexisting between blocks 36, 38 and blocks 36, 40 will be explainedshortly.

As can be seen particularly well in FIG. 2, with respect to the four,previously-mentioned outputs, output 20 comes from block 52, andprovides output information related to the text which appears within theimage of block 52 in FIG. 2. Output 22, similarly, provides informationcharacterized by the language which appears in the image of block 56 inFIG. 2 to which it is connected. Outputs 24 26, respectively, provideoutput information as identified by the texts appearing, respectively,in the images of blocks 58, 38, respectively, to which these two outputsare connected.

With heart-sound and ECG fiducial data, and appropriate CHMM settingsinformation, introduced as identified in FIG. 2 into algorithm 12 viainputs 14, 16, 18, the wavelet transform and pattern recognitionmethodology of this invention for analyzing a subject's heart soundstakes place, as is now described.

When an analysis is to be made in accordance with the practice of thisinvention respecting the condition of a subject's heart, an appropriatetime frame, such as a time frame of about 10 seconds, is selected forthe gathering of heart-sound and ECG fiducial data, preferably performednon-invasively. Heart-sound data is and acquired by the conventionaltechnique of digital sampling, and this is done at a rate preferably ofabout 500-Hz—a rate which has been found to be quite suitable. This500-Hz sampling rate is also referred to herein as a first definedsampling rate. Sampled heart sound information is fed via input 14 toblocks 28 and 54 in FIG. 2. Simultaneously, ECG fiducial information isalso appropriately gathered, prepared and delivered, as seen in FIG. 2,to blocks 40 and 58. CHMM settings data, described earlier herein, isfurnished via input 18 to block 46 in FIG. 2.

With respect to the operation of block 28 on received heart-soundinformation, this block computes what is referred to herein as themaximum-overlap discrete wavelet transform (MODWT), also known in theart as a stationary wavelet transform, specifically to develop waveletscales I-VI, using, as mentioned above in the definitions sections ofthis disclosure, the conventionally known Length-8 wavelet filter fromthe Daubechies Least Asymmetric Family of wavelet filters. These six,developed wavelet scales are then fed from block 28, via the sixarrow-headed connections shown extending between blocks 28, 30, to block30 which functions to locate, in time, the peaks of the absolute valuesof the MODWT coefficients for each wavelet scale.

Following the operation of block 30, wavelet scale information relevantto what has taken place within this block is furnished, as illustratedin FIG. 2, first to block 32, and thence through block 32 to block 34.Within block 32, and for each wavelet scale, an interpolation takesplace between the peak information developed within block 30.Subsampling for each wavelet scale is then performed with respect tointerpolated signal information at a uniform sampling rate which, underall circumstances, is no greater than the mentioned first definedsampling rate of 500-Hz. In fact, subsampling performed here which isrelevant to the six different wavelet scales is performed atspecifically different sampling rates, the values of which are relatedto the specific frequency bands associated with each of the sixdifferent wavelet scales. Reference here is made back to the Definitionssection of this disclosure for a reminder at this point of how thewavelet scales are frequency-banded, and what specific subsamplingrates, also referred to herein as second defined sampling rates, arespecifically employed with respect to each of the six different waveletscales.

Within block 34, a mathematical operation is performed to compute thelogarithms of the uniformly sampled values developed by the operation ofblock 32. Logarithmic information developed by the operation of block34, for each of the six wavelet scales, is furnished to block 36 whichthen computes the average of all log values within each wavelet scale,with this average then being subtracted from each log value on awavelet-scale-by-wavelet-scale basis.

Looking at the flow of information which emerges from block 36, andwhich is supplied to block 40, here one will note, as mentioned earlier,that only five arrowheaded lines are employed to illustrate thiscommunication path. The reason for this is that, at this point in theoperation of this invention, the only information transmitted betweenblocks 36, 40 is that information which relates to wavelet scales II-VI,inclusive.

Several important operations take place within the confines of block 40.More specifically, within this block a computation takes place todevelop a series of what are referred to herein as feature vectors, andthis is done by aggregating the log values developed in the operation ofblock 36 within a series of 50-millisecond temporal windows, referred toherein as window frames, or just as frames. Specifically, the chosenframes for this purpose overlap with one another in such a fashion thatthe beginning of each frame is timed to take place about 12-millisecondsafter the beginning of a previous frame, assuming, of course, that therehas been a previous frame. This results in a feature vector which iscomputed in this fashion being generated essentially every12-milliseconds.

Also taking place within the operation of block 40 is the act ofappending to each vector the following three elements: (a) the time fromthe previous QRS onset to the time at the middle of the relevant frame;(b) the time from the previous P-wave onset to the beginning of theframe; and (c) the time from the end of the relevant time frame to thetime of the beginning of the subsequent QRS onset.

Still describing what occurs within block 40, for each frame, the firstthree elements of each vector are replaced by a linear transformation ofthose three elements. This transformation consists of multiplying thethree-element sub-vector by an appropriate matrix designed,approximately to diagonalize the co-variance matrix of the first threeelements of each vector, as computed across all frames of all data. Inthe relevant and well understood art, which is, per se, conventionalart, this practice is known as a whitening transformation.

Finally, with respect to the operation of block 40, for each frame,there is appended to the vector as delta features the temporaldifference between consecutive wavelet values, but only with respect towavelet scales III, IV and V.

Within block 42, which receives output information from block 40, andfor each frame, a computation takes place to determine themultidimensional probability density function for each sound candidate(S1, S2, S3, S4). These functions are embodied by a Gaussian mixturemodel, and the output of each density function is referred to herein asa score.

From block 42, processed signal information is fed to block 44, wherein,by using the median of the seven highest scores in each frame, asynthesis of the scores takes place for sound candidates which do nothave explicit probability density function models. This synthesizingprocess, and the nature of the resulting output information followingsynthesis, is well understood by those skilled in the art.

Output information from block 44 flows as shown to Viterbi search block46 which also receives the previously mentioned CHMM settings data viainput 18. Using the CHMM information, and a Viterbi search, processingtakes place to compute the posterior probability of each sound candidateoccurring during each frame, given all of the available data, and forthe purpose of finding the maximum likelihood sequence of candidatesounds evident in the available data.

From block 46, output information is fed, as can be seen in FIG. 2, toblocks 48, 50 and 52.

Within block 48 a conversion takes place to convert the sequence ofheart sounds into a desired segmentation of the input and receivedheart-sound data.

Within block 50 a modification of the segmentation performed in block 48takes place to ensure consistency of sounds between different cardiaccycles. Here, any heart-sound S3 and S4 segments which are found to haveabnormal timing, or which are followed by apparent excessive noise, areomitted from further treatment.

Outputs from block 50 are supplied, as shown, to blocks 38 52 and 56.

Within block 38, and only in relation to wavelet scales numbers I, IIand III, the first and second valve closure times within each previouslyidentified heart-sound S1 and heart sound S2 segment are determinedusing information supplied from block 36. In block 56 a determination ismade respecting the amplitude of the bandpass-filtered waveform withineach segment. The relative bandpass filter as employed herein has ahighpass cutoff at about 22-Hz and a lowpass cutoff at about 125-Hz. Theamplitude is the difference between the highest and lowest values in therelevant segment.

In block 52, a determination is made to establish the overall presenceor absence of heart sounds S3 and S4, and of summation sounds based uponall the available data. Also determined within this block is the overallconfidence of scores associated with those S3 and S4 determinations.This overall confidence score for each sound is formed by summing theprobability scores from individually identified S3, S4 and summationsegments, respectively, along with the scores of heartbeats which didnot contain such segments. This determined, overall confidence score isthen compared to a suitable, user-selectable threshold so as todetermine the overall presence or absence of the relevant heart sound.

Finally, within block 58, and based upon the several inputs providedthis block as seen in FIG. 2, calculations take place to establish whatwere previously described as extended measurements. Such calculationsare quite familiar to those skilled in the relevant art.

Thus, from the high-level description which has just been given, and thefact that the internal operations of each of the blocks shown in FIG. 2are well-known to those skilled in the art, one can observe how the fourdifferent categories of output information are developed, respectively,on outputs 20, 22, 24, 26. There has thus now been described a preferredembodiment of, and manner of practicing the present invention.

From a relatively high-level point of view, core, cooperative featuresof the invention methodology can be described as offering a wavelettransform and pattern recognition method for analyzing a subject's heartsounds, including the steps of (a) obtaining heart-sound-relevantacoustic data (heart-sound data) from a selected subject, utilizing afirst defined sampling rate, (b) simultaneously gathering ECG data,including pre-selected ECG fiducial data, and (c) processing all of thisdata, including, with respect to the obtained acoustic data, (1)computing the maximum-overlap discrete wavelet transform (MODWT), (2)locating in time the peaks of the absolute values of the MODWTcoefficients respecting each of a pre-selected number (herein six) ofwavelet scales, and (3), for each predetermined wavelet scale,interpolating between the located peaks, and subsampling the results ofthat interpolating behavior, a second defined sampling rate which is nogreater than the mentioned first defined sampling rate.

Accordingly, while a preferred embodiment of and manner of practicingthe methodology of the present invention have been described andillustrated herein, it is appreciated that variations and modificationsmay be made without departing from the spirit of the invention.

I claim:
 1. A wavelet transform method for analyzing a subject's heartsounds for the purpose of aiding in assessing the condition of thesubject's heart, said method comprising employing a heart sound detectorutilizing a first defined sampling rate, obtaining heart-sound-relevantacoustic data from the subject, and processing obtained acoustic dataincluding (a) computing the maximum-overlap discrete wavelet transform(MODWT) for a preselected number of wavelet frequency-band scales havingcoefficients, (b) determining, and locating the peaks in time of, theabsolute values of the MODWT coefficients respecting each frequency-bandscale, and (c), for each wavelet frequency-band scale, (1) interpolatingbetween the located peaks, and (2) subsampling the result of saidinterpolating at another defined sampling rate which is both specific tothe relevant frequency-band scale, and no greater than the mentionedfirst defined sampling rate.
 2. A wavelet transform method for analyzinga subject's heart sounds for the purpose of aiding in assessing thecondition of the subject's heart, said method comprising employing aheart sound detector utilizing a first defined sampling rate, obtainingheart-sound-relevant acoustic data from the subject, and processingobtained acoustic data including (a) establishing six, preselectedwavelet frequency-band scales having coefficients using the Length-8wavelet filter from the Daubechies Least Asymmetric Family of waveletfilters (b) computing the maximum-overlap discrete wavelet transform(MODWT) for these six scales, (c) determining, and locating the peaks intime of, the absolute values of the MODWT coefficients respecting eachscale, and (d), for each scale, (1) interpolating between the locatedpeaks, and (2) subsampling the result of said interpolating at anotherdefined sampling rate which is both specific to the scale, and nogreater than the mentioned first defined sampling rate.
 3. The method ofclaim 2, wherein the six wavelet scales are designated I-VI, inclusive,with scale I having a 3-db filtered range of about 124-250-Hz, scale IIa like-filtered range of about 62-124-Hz, scale III a like-filteredrange of about 31-62-Hz, scale IV a like-filtered range of about16-31-Hz, scale V a like-filtered range of about 8-16-Hz, and scale VI alike-filtered range of about 4-8-Hz.
 4. The method of claim 3, whereinthe first defined sampling rate is no less than about 500-Hz, and thereis a specific, and differentiated, second defined sampling rateassociated with each of the six wavelet scales, with that which isassociated with scale I being no greater than about 500-Hz, that whichis associated with scale II being no greater than about 250-Hz, thatwhich is associated with scale III being no greater than about166.67-Hz, that which is associated with scale IV being no greater thanabout 83.33-Hz, that which is associated with scale V being no greaterthan about 41.67-Hz, and that which is associated with scale VI being nogreater than about 21.74-Hz.
 5. A wavelet transform method for analyzinga subject's heart sounds for the purpose of aiding in assessing thecondition of the subject's heart, said method comprising employing aheart sound detector utilizing a first defined sampling rate, obtainingheart-sound-relevant acoustic data from the subject, processing obtainedacoustic data including (a) computing the maximum-overlap discretewavelet transform (MODWT) for a preselected number of waveletfrequency-band scales having coefficients, (b) determining, and locatingthe peaks in time of, the absolute values of the MODWT coefficientsrespecting each scale, and (c), for each preselected wavelet scale, (1)interpolating between the located peaks, and (2) subsampling the resultof said interpolating at a another defined sampling rate which is bothspecific to the scale, and no greater than the mentioned first definedsampling rate, which subsampling produces subsampling values, computingthe logarithm of the subsampling values produced by said subsampling togenerate log values, for each wavelet scale, computing the mean averageof all related, generated log values, and subtracting the computed meanaverage from each log value to produce a set of associated, resultingvalues.
 6. The method of claim 5, which further comprises, for each oneof the preselected wavelet scales, computing a series of feature vectorsby aggregating the wavelet-scale-associated resulting values existingwithin a series of defined-length, next-adjacent temporal window frameswhich overlap with one another.
 7. The method of claim 6, wherein thedefined length of each temporal window frame is about 50-milliseconds,and overlapping between next-adjacent window frames is such that thestart of an overlapping window frame begins about 12-milliseconds afterthe start of the just-previous window frame.
 8. The method of claim 6,wherein the preselected ECG fiducials include the respective times ofQRS and P-wave onsets, and which further includes appending to eachfeature vector (a) the time difference between the time of the previousQRS onset to the time of the middle of the relevant associated windowframe, (b) the time difference between the time of the previous P-waveonset to the time of the beginning of the relevant, associated windowframe, and (c) the time difference between the time of the end of therelevant, associated window frame to the time of the next subsequent QRSonset.
 9. The method of claim 6, which further includes, for each windowframe, computing a multi-dimensional probability for each heart-soundcandidate, and utilizing each computed probability as a score.
 10. Themethod of claim 9, which further includes utilizing a Constrained HiddenMarkov Model and the activity of a Viterbi search to compute a posteriorprobability associated with each heart-sound candidate.
 11. The methodof claim 10, which further includes effectively using informationcontained in the mentioned subtraction-produced resulting valve closuretimes which take place in prior-noted heart-sound-S1 and heart-sound S2segments of the obtained subject data.
 12. The method of claim 10, whichfurther includes producing a continuously valued overall confidencescore relating to the S3 and S4 heart sounds, with said confidencescore-producing being based upon (a) use of the Constrained HiddenMarkov Model, and (b) summation of at least the mentioned, computedprobability function scores associated with the S3 and S4 heart sounds.